Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model.
Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model.
复制标题
使用 PFEEL 算法和高斯过程回归模型对影响进行概率检测。
DOI：
10.1016/j.engstruct.2023.116255
复制
发表时间：
2023
影响因子：
5.5
通讯作者：
Franco,JeanM
中科院分区：
文献类型：
--
作者：
MejiaCruz,Yohanna;Caicedo,JuanM;Jiang,Zhaoshuo;Franco,JeanM
识别人类活动的方法具有广泛的潜在应用，包括安全、事件时间检测、智能建筑环境和人类健康。目前的方法通常依赖于波传播或结构动力学原理。然而，基于力的方法，如概率力估计和事件定位算法（PFEEL），通过避免多径衰落等挑战，比波传播方法具有优势。PFEEL利用概率框架来估计撞击的力量和校准空间中的事件位置，在估计中提供不确定性的测量。本文提出了一种基于高斯过程回归（GPR）的数据驱动模型实现PFEEL的新方法。新方法是通过在一个铝板上收集的实验数据来评估的，该铝板在81个点上受到冲击，间隔为5厘米。结果以不同概率水平下相对于实际撞击位置的局部化区域的形式呈现。这些结果可以帮助分析人员确定各种PFEEL实现所需的精度。
Methods for identifying human activity have a wide range of potential applications, including security, event time detection, intelligent building environments, and human health. Current methodologies typically rely on either wave propagation or structural dynamics principles. However, force-based methods, such as the probabilistic force estimation and event localization algorithm (PFEEL), offer advantages over wave propagation methods by avoiding challenges such as multi-path fading. PFEEL utilizes a probabilistic framework to estimate the force of impacts and the event locations in the calibration space, providing a measure of uncertainty in the estimations.This paper presents a new implementation of PFEEL using a data-driven model based on Gaussian process regression (GPR). The new approach was evaluated using experimental data collected on an aluminum plate impacted at eighty-one points, with a separation of five centimeters. The results are presented as an area of localization relative to the actual impact location at different probability levels. These results can aid analysts in determining the required precision for various implementations of PFEEL.